Error during deeplabv3+ fine tuning using the CamVid example

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Giovanni
Giovanni 2023-9-26
回答： Avadhoot 2023-10-5
I'm trying to fine tune a deeplabv3+ net following the CamVid example. I reduced the mini batch size from 16 to 2 but still having the following error:
Error using trainNetwork (line 184)
Error using 'backward' in layer nnet.cnn.layer.Resize2DLayer. The function threw an error and could not
be executed.
Caused by:
Error using *
An unexpected error occurred on the device. The error code was: UNKNOWN_ERROR
Is it due to GPU memory limitations? I have a laptop with a rtx 4060 laptop (8gb), 16Gb ram and an I7 12700h. When running with batch size equals to 4, it seems that the gpu memory spikes to 4GB, then MATLAB returns the error above.
UPDATE:
I do not have this error when running the code on my desktop pc with i5 13600k, 32gb ram and rtx 3070 (8gb) using a mini batch size = 8. In this case Matlab is version r2022b.
%% Configure Pretrained DeepLabv3+ Network for Transfer Learning
% The following code demonstrates configuring a pretrained
% DeepLabv3+[1] network on the custom dataset.
%% Download Pretrained Model
model = helper.downloadPretrainedDeepLabv3Plus;
net = model.net;
%% Download CamVid Dataset
% This example uses the CamVid dataset[2] from the University of Cambridge for training.
% This dataset is a collection of images containing street-level views obtained while
% driving. The dataset provides pixel-level labels for 32 semantic classes including car,
% pedestrian, and road.
%
% Download the CamVid dataset from the following URLs.
imageURL = 'http://web4.cs.ucl.ac.uk/staff/g.brostow/MotionSegRecData/files/701_StillsRaw_full.zip';
labelURL = 'http://web4.cs.ucl.ac.uk/staff/g.brostow/MotionSegRecData/data/LabeledApproved_full.zip';
outputFolder = fullfile(tempdir,'CamVid');
labelsZip = fullfile(outputFolder,'labels.zip');
imagesZip = fullfile(outputFolder,'images.zip');
if ~exist(labelsZip, 'file') || ~exist(imagesZip,'file')
mkdir(outputFolder)
disp('Downloading 16 MB CamVid dataset labels...');
websave(labelsZip, labelURL);
unzip(labelsZip, fullfile(outputFolder,'labels'));
disp('Downloading 557 MB CamVid dataset images...');
websave(imagesZip, imageURL);
unzip(imagesZip, fullfile(outputFolder,'images'));
end
% Note: Download time of the data depends on your Internet connection. The commands
% used above block MATLAB until the download is complete. Alternatively, you can
% use your web browser to first download the dataset to your local disk. To use
% the file you downloaded from the web, change the 'outputFolder' variable above
% to the location of the downloaded file.
%% Load CamVid Images
imgDir = fullfile(outputFolder,'images','701_StillsRaw_full');
imds = imageDatastore(imgDir);
%% Load CamVid Pixel-Labeled Images
% To make training easier, the 32 original classes in CamVid are grouped into
% 11 classes as follows. To reduce 32 classes into 11, multiple classes from the
% original dataset are grouped together. For example, "Car" is a combination of
% "Car", "SUVPickupTruck", "Truck_Bus", "Train", and "OtherMoving".
classes = [
"Sky"
"Building"
"Pole"
"Road"
"Pavement"
"Tree"
"SignSymbol"
"Fence"
"Car"
"Pedestrian"
"Bicyclist"
];
% Return the grouped label IDs by using the helper function 'camvidPixelLabelIDs'.
labelIDs = helper.camvidPixelLabelIDs;
% Use the classes and label IDs to create the pixelLabelDatastore.
labelDir = fullfile(outputFolder,'labels');
pxds = pixelLabelDatastore(labelDir,classes,labelIDs);
%% Analyze Dataset Statistics
% To see the distribution of class labels in the CamVid dataset, use 'countEachLabel'.
% This function counts the number of pixels by class label.
tbl = countEachLabel(pxds);
% Ideally, all classes would have an equal number of observations. However,
% the classes in CamVid are imbalanced, which is a common issue in automotive
% data-sets of street scenes. Such scenes have more sky, building, and road pixels
% than pedestrian and bicyclist pixels because sky, buildings and roads cover
% more area in the image. If not handled correctly, this imbalance can be detrimental
% to the learning process because the learning is biased in favor of the dominant
% classes. To handle this issue, class weighting has been used.
%% Prepare Training, Validation, and Test Sets
% Deeplabv3+ is trained using 60% of the images from the dataset. The rest
% of the images are split evenly in 20% and 20% for validation and testing
% respectively. The following code randomly splits the image and pixel label
% data into a training, validation and test set.
[imdsTrain, imdsVal, imdsTest, pxdsTrain, pxdsVal, pxdsTest] = helper.partitionCamVidData(imds,pxds);
%% Configure Pretrained Network
% To configure the DeepLabv3+ network for transfer learning, you should replace
% the last convolutional layer and pixelClassificationLayer in the layergraph
% obtained from the pretrained model.
% Specify the number of classes.
numClasses = numel(classes);
% Extract the layergraph from the pretrained network to perform custom
% modification.
lgraph = layerGraph(net);
% Replace the last convolution layer in the pretrained network with the new
% convolution layer.
convLayer = convolution2dLayer([1 1], numClasses,'Name', 'node_398');
lgraph = replaceLayer(lgraph,"node_398",convLayer);
% Balance classes using class weighting.
imageFreq = tbl.PixelCount ./ tbl.ImagePixelCount;
classWeights = median(imageFreq) ./ imageFreq;
% Replace the pixel classification layer in the pretrained network with the classweights
% and new pixel classification layer.
pxLayer = pixelClassificationLayer('Name','labels','Classes',tbl.Name,'ClassWeights',classWeights);
lgraph = replaceLayer(lgraph,"labels",pxLayer);
% Use analyzeNetwork to visualize the new network.
analyzeNetwork(lgraph);
%% Data Augmentation
% Data augmentation is used to improve network accuracy by randomly transforming
% the original data during training. By using data augmentation, you can add
% more variety to the training data without increasing the number of labeled
% training samples.
%
% This pretrained model has input size of [513,513,3] and the CamVid images
% are of size [720,960,3]. Hence, it would be better to use random patches
% of size [513,513,3] from the given input images for training.
%
% In this case, 'randomPatchExtractionDatastore' is useful for creating
% such training and validation datastores.
%
% To apply the same random transformation to both image and pixel label data
% use 'imageDataAugmenter' object in 'DataAugmentation' NVP during creating
% 'randomPatchExtractionDatastore' object. Here, random left/right reflection
% and random X/Y translation of +/- 10 pixels is used for data augmentation.
xTrans = [-10 10];
yTrans = [-10 10];
augmenter = imageDataAugmenter('RandXReflection',true, 'RandXTranslation',xTrans, 'RandYTranslation',yTrans);
dsTrain = randomPatchExtractionDatastore(imdsTrain,pxdsTrain,[513 513],'PatchesPerImage',8, 'DataAugmentation', augmenter);
% Note that data augmentation is not applied to the test and validation data.
% Ideally, test and validation data should be representative of the original
% data and is left unmodified for unbiased evaluation.
%% Select Training Options
% The optimization algorithm used for training is stochastic gradient descent
% with momentum (SGDM). Use trainingOptions to specify the hyper-parameters
% used for SGDM.
% Define validation datastore.
dsVal = randomPatchExtractionDatastore(imdsVal,pxdsVal,[513 513],'PatchesPerImage',8);
% Define training options.
options = trainingOptions('sgdm', ...
'LearnRateSchedule','piecewise',...
'LearnRateDropPeriod',10,...
'LearnRateDropFactor',0.3,...
'Momentum',0.9, ...
'InitialLearnRate',1e-3, ...
'L2Regularization',0.005, ...
'ValidationData',dsVal,...
'MaxEpochs',6, ...
'MiniBatchSize',2, ...
'Shuffle','every-epoch', ...
'CheckpointPath', tempdir, ...
'VerboseFrequency',2,...
'Plots','training-progress',...
'ValidationPatience', 4,...
'ExecutionEnvironment','auto');
% The learning rate uses a piecewise schedule. The learning rate is reduced
% by a factor of 0.3 every 10 epochs. This allows the network to learn quickly
% with a higher initial learning rate, while being able to find a solution
% close to the local optimum once the learning rate drops.
%
% The network is tested against the validation data every epoch by setting
% the 'ValidationData' parameter. The 'ValidationPatience' is set to 4 to
% stop training early when the validation accuracy converges. This prevents
% the network from overfitting on the training dataset.
%
% A mini-batch size of 16 is used for training. You can increase or decrease
% this value based on the amount of GPU memory you have on your system.
%
% In addition, 'CheckpointPath' is set to a temporary location. This name-value
% pair enables the saving of network checkpoints at the end of every training
% epoch. If training is interrupted due to a system failure or power outage,
% you can resume training from the saved checkpoint. Make sure that the location
% specified by 'CheckpointPath' has enough space to store the network checkpoints.
% Now, you can pass the 'dsTrain', 'lgraph' and 'options' to trainNetwork
% as shown in 'Start Training' section of the example 'Semantic Segmentation
% Using Deep Learning' to obtain deepLabv3+ model trained on the custom dataset.
%
% You can follow the sections 'Test Network on One Image' for inference using
% the trained model and 'Evaluate Trained Network' for evaluating metrics.
%% References
% [1] Chen, Liang-Chieh et al. “Encoder-Decoder with Atrous Separable Convolution
% for Semantic Image Segmentation.” ECCV (2018).
%
% [2] Brostow, G. J., J. Fauqueur, and R. Cipolla. "Semantic object classes
% in video: A high-definition ground truth database." Pattern Recognition Letters.
% Vol. 30, Issue 2, 2009, pp 88-97.
%
% Copyright 2021 The MathWorks, Inc.

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回答（1 个）

Avadhoot
Avadhoot 2023-10-5
Hi Giovanni,
I understand that you are encountering an error while training a DeepLabV3+ model in MATLAB R2021b. The error is likely due to GPU memory limitations. There are several factors that could contribute to this issue:
  1. DeepLabV3+ Model: The DeepLabV3+ model itself is quite large, especially with the Xception backbone. The size of the weights can contribute to the memory limitations.
  2. Image Resolution: The memory requirements are dependent on the input image resolution. Higher-resolution images require more memory. You can try reducing the resolution to alleviate the memory constraints.
  3. Preprocessing and Optimizers: Image preprocessing steps, such as data augmentation or resizing, can also impact the memory requirements. Additionally, the choice of optimizer used for training the neural network can influence memory usage.
Despite reducing the batch size, if the model is still not training, these factors could be the underlying causes.
I recommend trying the following steps to address the memory limitations:
  1. Reduce the image resolution to lower the memory requirements.
  2. Consider using a smaller backbone network architecture or a pre-trained model with lower memory requirements.
  3. Optimize the preprocessing steps to minimize memory usage.
  4. Experiment with different optimizers or learning rate schedules to find a configuration that works within the available memory constraints.
Please refer to the following answer on “MATLAB Answer” as the issue here issimilar to the one you are facing:
https://www.mathworks.com/matlabcentral/answers/897932-how-do-i-solve-an-unknown-error-using-gpu-array-error-using-gpuarray-an-unexpected-error-occurred
I hope it helps,
Regards,
Avadhoot

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